Featured Video
This Week in Quality Digest Live
Management Features
Harish Jose
The dangers of misapplying linearity
James daSilva
Like it or not, these are the good times
Chad Kymal
A single set of FMEA requirements will ease the burden on suppliers
Michelle LaBrosse
Projects go more smoothly if you have a consistent process for doing them
Rob Magee
The modern security mindset

More Features

Management News
Management's role in improving work climate and culture
Work with and learn from some of the nation’s best people and organizations
Cricket Media and IEEE team up to launch TryEngineering Together
125 strategies to achieve maximum confidence, clarity, certainty, and creativity
MIT awards more than $1 million to organizations creating greater economic opportunity for workers
Earn continuing education units
If you want to understand a system, try and change it
How to engage, retain, and develop talent for maximum performance

More News

Management

When to Abandon an Unresolved Project

It boils down to knowing your own speed

Published: Monday, November 13, 2017 - 12:01

Academics and corporate innovators both spend their workdays pursuing breakthroughs that may never materialize. Venturing into unknown territory carries fairly high potential rewards, but also a fairly high risk of failure.

When working on a research project, it can be difficult to decide when to cut your losses. Optimism tells us that just beyond our grasp hovers the solution that will make it all work; pessimism, on the other hand, tells us that the end we have in sight may well be a dead end. We are always aware that every day spent chasing a mirage wastes valuable time and resources. Absent a glaring signpost of failure, how does one know when it makes strategic sense to abandon an idea?

Our new paper in Operations Research helps answer this question systematically. (See an earlier version of this research.) Although uncertainty is a given in any speculative project, the good news is that we can still base our decisions on something more solid than guesswork and intuition.

Search and development

Our paper presents a prescriptive analytical model designed to mirror a researcher’s real-life situation. Let’s say there is a risky project being worked on, with no reliable information regarding how close to completion the project actually is. The option to drop the project and resume searching for a more worthwhile endeavor is always available. We model the trade-off between the cost of giving up too early (thereby forfeiting potentially valuable rewards) and the cost of letting more promising opportunities pass by. How should the researcher determine the right time to stop?

Across several trials, we varied the most relevant parameters, including the number of potential rewards (single vs. multiple), the ability to revisit previously abandoned ideas, and the risk tolerance of decision makers.

A consistent finding was that one primary cause of uncertainty in these cases—the probability of success in a given project—was not as important as one might think. The optimal stopping time is insensitive to the probability of success, as long as this probability is not too high (e.g., below 50 percent). This is a broad enough range to include most creative endeavors. Certainly, it would be foolishly optimistic to think that exploration in, say, the pharmaceutical industry has a greater than 30-percent chance of producing the next wonder drug.

To obtain a fairly accurate estimate of when you should move on, you need to know only two things: the arrival rate of new projects (i.e., the speed at which your search process generates results), and the arrival rate of success (i.e., how quickly you are usually able to bring a project to completion). It boils down to knowing your own speed. As you pick up the pace in your search and exploration activities, you can reduce the time you’re willing to devote to an unresolved project.

Discuss

About The Authors

Kevin McCardle’s picture

Kevin McCardle

Kevin McCardle is a professor of decisions, operations, and technology management at the UCLA Anderson School of Business.

Ilia Tsetlin’s picture

Ilia Tsetlin

Ilia Tsetlin is a professor of decision sciences at INSEAD.

Robert Winkler’s picture

Robert Winkler

Robert Winkler is a James B. Duke Professor at the Duke Fuqua School of Business.

Comments

Research Paper

Thank you so much for providing a link to your research paper and summarizing your recommendations in 2 steps:

1. The arrival rate of new projects.

2. The arrival rate of success.

In my workplace, the huge problem is that no one ever seems to "complete" projects. Hence, our success rate is very low.